Significant reductions in vehicle emissions can be achieved, congestion can be limited, safety can be enhanced and travel times reduced by integrating diverse technology in the vehicular transportation domain. Numerous schemes have been proposed in the past for informing drivers of traffic conditions and presenting them with proposed alternatives when congestion is found. For example, traffic helicopters have been used for decades by radio stations to spot areas of congestion and suggest alternate paths that drivers may wish to consider.
With the growing popularity of GPS and hand-held computing devices, particularly those connected to cellular networks or the internet, other approaches have been used, such as graphical representations of maps with routes being color-coded to indicate levels of congestion.
Another approach to the traffic congestion problem involves “smart” traffic signals. For instance, railroad crossings have for decades been tied to traffic signals to help ease the flow of traffic on routes adjacent to railroad crossings when a train approaches. Further, certain systems have been installed that allow emergency vehicles such as fire trucks to change the state of a light from red to green so that the emergency vehicle can cross the intersection quickly with, rather than against, the signal.
In still another related area, various attempts have been made to collect traffic information from drivers who have, for example, GPS-enabled smartphones with them in their vehicles. Typically, such drivers do not find sufficient incentive to start up, and keep running, an application that will transmit their speed and location information to a remote traffic database.
Systems are emerging that take advantage of the integration of technologies that are available to report traffic information to drivers and suggest routes based on that information, to communicate with traffic signals, and to collect traffic information from drivers. For example, a project known as the Cooperative Intersection Collision Avoidance system for Violations (CICAS-V) sought to predict stop sign and traffic signal violations and warn the driver of the impending problem. See, e.g., Cooperative Intersection Collision Avoidance System for Violations (CICAS-V) for Avoidance of Violation-Based Intersection Crashes, Michael Maile and Luca Delgrossi (Mercedes-Benz Research & Development North America, Inc.), Paper Number 09-0118, downloaded from http://www-nrd.nhtsa.dot.gov/pdf/esv/esv21/09-0118.pdf for an exemplary research report from this project. As a follow-up to that work, research has been conducted into optimal timing for prediction of such intersection violations and for issuing warnings relating to same. See, e.g., Behavior Classification Algorithms at Intersections and Validation using Naturalistic Data, George Aoude, Vishnu Desaraju, Lauren Stephens and Jonathan How (Massachusetts Institute of Technology), presented at Intelligent Vehicles Symposium, June 2011 and downloaded from http://acl.mit.edu/papers/IV11AoudeDesarajuLaurensHow.pdf. These approaches are helpful, but rely on a level of direct communication among various infrastructure elements (traffic signals, vehicles, pedestrians) that may not be available for a number of years at many intersections.
In one particular area addressed by this disclosure, it would be advantageous to provide a system that minimizes reliance on infrastructure communications yet still enhances safety by, for instance, warning drivers and pedestrians automatically when a vehicle's current course and speed suggest that it is going to pass a traffic control illegally (e.g., go through an intersection when a traffic light is red).